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Small-Scale Variability in Bacterial Community Structure in Different Soil Types
Mylène Hugoni, Naoise Nunan, Jean Thioulouse, Audrey Dubost, Danis Abrouk, Jean Martins, Deborah Goffner, Claire Prigent-Combaret, Geneviève
Grundmann
To cite this version:
Mylène Hugoni, Naoise Nunan, Jean Thioulouse, Audrey Dubost, Danis Abrouk, et al.. Small-Scale Variability in Bacterial Community Structure in Different Soil Types. Microbial ecology, Springer Verlag, 2021, �10.1007/s00248-020-01660-0�. �hal-03224926�
Small scale variability in bacterial communities structure in different soil 1
types 2
3
Mylène Hugoni1, Naoise Nunan2,3, Jean Thioulouse4, Audrey Dubost1, Danis Abrouk1, Jean 4
M.F. Martins5, Deborah Goffner6, Claire Prigent-Combaret1, Geneviève Grundmann1 5
6
1 Univ Lyon, Université Claude Bernard Lyon 1, CNRS, INRAE, VetAgro Sup, UMR Ecologie 7
Microbienne, F-69622 Villeurbanne, France 8
2 Institute of Ecology and Environmental Sciences – Paris, CNRS – Sorbonne Université, 4 9
place Jussieu, 75005 Paris, France 10
3 Department of Soil and Environment, Swedish University of Agricultural Sciences, 75007 11
Uppsala, Sweden 12
4 Biométrie et Biologie Evolutive, Université Lyon 1; CNRS UMR 5558; 69622 Villeurbanne 13
Cedex, France 14
5 Université Grenoble Alpes; CNRS; IRD; IGE UMR 5001; 38000 Grenoble, France 15
6 Unité Mixte Internationale CNRS 3189 « Environment, Health and Societies », Faculté de 16
Médecine, 51 Bd Pierre Dramard, 13344 Marseille, France 17
18
Correspondance: Mylène Hugoni, Université Lyon 1; CNRS, UMR5557; Ecologie 19
Microbienne; INRA, UMR1418; 69220 Villeurbanne Cedex, France. Email:
20
mylene.hugoni@univ-lyon1.fr ; ORCID number 0000-0002-2430-1057 21
22
Keywords: Bacterial diversity / Community structure / Soil / Spatial scale / Ecological 23
strategies 24
25
Abstract 26
Microbial spatial distribution has mostly been studied at field to global scales (i.e. ecosystem 27
scales). However, the spatial organization at small scales (i.e. centimeter to millimeter scales), 28
which can help improve our understanding of the impacts of spatial communities structure on 29
microbial functioning, has received comparatively little attention. Previous work has shown 30
that small scale spatial structure exists in soil microbial communities, but these studies have not 31
compared soils from geographically distant locations. Nor have they utilized community 32
ecology approaches, such as the core and satellite hypothesis and/or abundance-occupancy 33
relationships, often used in macro-ecology, to improve the description of the spatial 34
organization of communities. In the present work, we focused on bacterial diversity 35
(i.e.16SrRNA gene sequencing) occurring in micro-samples from a variety of locations with 36
different pedo-climatic histories (i.e. from semi-arid, alpine and temperate climates) and 37
physicochemical properties. The forms of ecological spatial relationships in bacterial 38
communities (i.e. occupancy-frequency and abundance-occupancy) and taxa distributions (i.e.
39
habitat generalists and specialists) were investigated. The results showed that bacterial 40
composition differed in the four soils at the small scale. Moreover, one soil presented a satellite 41
mode distribution whereas the three others presented bimodal distributions. Interestingly, 42
numerous core taxa were present in the four soils among which 8 OTUs were common to the 43
four sites. These results confirm that analyses of the small-scale spatial distribution are 44
necessary to understand consequent functional processes taking place in soils, affecting thus 45
ecosystem functioning.
46 47
48
49
1. Introduction 50
Microbial diversity is central to soil ecosystem functioning and the ecosystem services soils 51
deliver, as microbial communities are involved in many biogeochemical processes and their 52
various interactions may affect these activities [1–3]. Despite this fundamental role, we still do 53
not have a full understanding of the processes underpinning the emergence and maintenance of 54
soil microbial diversity. Hubbell (2001) defined biodiversity as being synonymous with species 55
richness and relative species abundance in space and time, where relative species abundance 56
refers to the commonness or rarity of a species in relation to others, in a given community. The 57
question of what makes a species common or rare has long been of empirical and theoretical 58
interest in community ecology [4]. Despite a growing number of surveys interested in rare and 59
core microbial communities, only a few recent studies have targeted bacterial spatial 60
distributions, in a range of different ecosystems, such as water, guts, soil or even the surface of 61
stone [5–9]. These studies have highlighted the fact the “rare biosphere” is constituted of a 62
myriad of species. The rare communities are of interest as they may be of functional 63
significance: it has been shown that rare taxa are a resource pool for responding to 64
environmental changes [10], maintaining species diversity [11], and promoting functional 65
redundancy [12]. Other studies conducted on aquatic ecosystems at a global scale have shown 66
the presence of a rich and diverse rare biosphere among Archaea and Bacteria [13, 14]. Hugoni 67
et al. (2013) reported that the rare archaeal biosphere in the ocean should not solely be 68
characterized as a seed bank of dormant cells; rather, it is a complex association of indigenous 69
and itinerant cell types of different origins and with different fates that might contribute to 70
microbial interaction networks and metabolic processes in the environment.
71
Although these have yielded a host of information at larger scales, microbial 72
distribution patterns and their consequences for ecosystem functioning are far less documented 73
at small scales [15–17], especially in soils. However, it is clear that cell-to-cell, cell-substrate 74
and cell-substratum interactions that all take place at small scales, are likely to have significant 75
effects on overall processes [18, 19]. Previous studies have attempted to study bacterial 76
communities and soil properties at sub-millimeter scales, in different contexts as different soil 77
aggregate size classes, aggregate surfaces vs interiors, various soil fractions, or different soil 78
pore distributions [20–25]. These studies, using several methods such as fingerprinting [21], 79
clone libraries [24], pyrosequencing [20, 22, 25] or particulate organic matter characterization 80
(POM) [23] suggested that microaggregates represent micro-environments that select specific 81
bacterial lineages [26]. In order to evaluate the regulatory role of such interactions, the problem 82
of spatial patterns and scale needs to be addressed and models derived from the community 83
ecology of macro-organisms might give the tools to understand small-scale microbial 84
biodiversity.
85
The analysis of how biodiversity is distributed across space has employed various 86
spatial approaches in community ecology, based on habitat generalists and specialists [27, 28], 87
and on the core-satellite hypothesis [29, 30]. Classifying species into habitat generalists and 88
specialists is the first step toward examining the underlying biological and ecological factors 89
leading to the differential distribution of species among habitats [31]. Habitat generalists are 90
defined as broadly distributed microbial taxa whereas specialists are rare taxa that can be locally 91
abundant [28, 32]. The “core-satellite hypothesis” (or “Hanski’s hypothesis”) proposes that 92
species fall into one of two categories: “core” species, which are widespread and locally 93
abundant, and “satellite” species consisting mostly of rare species that are present in a limited 94
number of sites and at low abundances [29, 30]. Core and satellite taxa are classically identified 95
to predict communities’ occupancy-frequency distributions (with occupancy defined as the 96
percentage of samples in which a taxon is present and frequency defined as the percentage of 97
taxa found for each occupancy). This characterization of communities’ ecology in terms of 98
spatial distribution often showed a bimodal distribution (i.e. most species are present in either 99
most patches or only a small number of patches). In contrast, generalists and specialists refer to 100
the ability of a taxon to tolerate a range of habitats by considering the spatial distribution of 101
taxa. Previous work focusing on soil bacteria at the small scale showed that a occupancy- 102
frequency relationship was detectable at this small scale, following Hanski’s core-satellite 103
hypothesis [8]. This work was done using microarray and 454 pyrosequencing data, using 104
samples of a few milligrams taken along a 22 cm long transect at one cm intervals. Here, we 105
extend this study to include a range of soils and use Illumina sequencing in order to derive an 106
more in depth analysis of the distribution of individual taxa. We chose to include a number of 107
soils with different abiotic properties because it is known that these properties influence the 108
composition and distribution of microbial communities.
109
Other works have attempted to identify which environmental variables shape microbial 110
diversity. Indeed, previous work reported a relationships between microbial distributions and 111
abiotic variables [33–35] and aimed to determine the main spatial community driver at a larger 112
scale.
113
The objective of this study was to investigate in different soils, at the small scale (from 114
1cm to 1m intervals and about 100 to 500mg weight) whether (1) soil bacterial communities 115
exhibit spatial structures and, if so, whether (2) these structures varied among soils from 116
different environments. We used Hanski’s hypothesis (the core and satellite hypothesis) to 117
structure the analysis. The four soils that were used in the study were from four pedo-climatic 118
situations and from three different regions: two Alpine polygonal soils (one from the centers 119
and the other from the sides of polygons), a soil from the Rhône area and a Sahel soil. Bacterial 120
diversity analyses, evaluated using a 16S rRNA gene metabarcoding approach, were carried 121
out. Community ecology approaches were used to assess the spatial structures using occupancy- 122
frequency distribution patterns (i.e. core or satellite mode distributions) and abundance- 123
occupancy relationships (abundance being calculated as the average percent presence of a taxon 124
in samples). An in-depth analysis of bacterial OTU spatial distribution patterns was conducted 125
by studying core taxa (defined as OTUs present in 100% of the micro-samples for a considered 126
soil) and satellite taxa (defined as present in only 1 micro-sample for a considered soil, these 127
satellite taxa were therefore spatially rare taxa). Moreover, in order to gain a better 128
understanding of the ecological strategies employed by the taxa with regard to their habitat, we 129
classified them according to their distribution across micro-samples (generalist taxa defined as 130
broadly distributed bacterial taxa and specialists taxa were satellite taxa with more than 0.1%
131
of sequences per micro-sample).
132 133
2. Materials and Methods 134
2.1. Soil sampling 135
Soils were sampled in regions presenting four pedo-climatic combinations in three geographic 136
locations: La Vanoise (Van), a loamy polygonal soil under alpine climate, sampled in the center 137
of polygons (Van_PC) and from the sides of polygons (Van_PS) (polygons being typical 138
structures retrieved in soil that experience freezing and thawing alternately); La Dombes (LD), 139
a silty loam soil under temperate climate and Grande Muraille Verte (GMV), a sandy soil in a 140
semi-arid climate.
141
Thirty nine micro-samples were collected at La Vanoise (col de la Laysse, La Vanoise, 142
France, 2200m altitude, N 45° 27’ 19.56’’, L 6° 51’ 54.12’’), characterized by an average 143
temperature in the summer of 5.3°C and an annual rainfall of 637mm. Seventeen micro-samples 144
were collected in Van_PC and 22 micro-samples were collected in Van_PS in September 2013 145
and September 2014 to take into account the soil convection movements typical of this type of 146
soil with polygons (Supplementary Table 1 [36]). Twenty-two LD soil micro-samples were 147
collected in an undisturbed agricultural soil in September 2013 (Saint André de Corcy, Ain, 148
France, 45° 51’ 04.7’’ N 4° 57’ 30.5’’) characterized by an average annual min-max 149
temperatures of 8.1°C-16.9°C and annual rain of 832mm. Finally, twenty soil micro-samples 150
were collected in September 2014 in a Savanna soil (Widou, Grande Muraille Verte, Senegal, 151
15°58.549’N 15°17.221’W). Maximum and minimum temperatures are 36.9°C and 20.5°C, 152
respectively, and the annual rainfall is 300mm.
153
The sampling procedure for the Van_PC, Van_PS and LD soils proceeded as follows:
154
soil cores, 1cm diameter and 2cm depth, were taken with a corer along a longitudinal transect, 155
with 1cm to 10m lags, 0.2 to 10m long (Supplementary Table 2). The top 1mm of the cores 156
(sample depth named “a”) and a 1 mm thick slice underneath (sample depth named “b”) were 157
cut with a scalpel blade. The soil water content allowed the sample to remain intact. Samples 158
were freeze-dried and stored at -80°C for DNA extraction. Two-centimeter depth core was an 159
intermediary size to sample in the field and to bring it back to lab and subsample the final 160
sample (i.e. the micro-sample) with spatial coordinate. Slice a was analyzed systematically and 161
slice b was taken in case there was a need to verify data.
162
In La Vanoise, micro-samples were collected along two 10m transects with lags from 163
1cm to 1m, to ensure that the sampling covered several polygons, from the centers (Van_PC, 164
yellow coloured) and sides of polygons (Van_PS, brown colored), respectively (Supplementary 165
Table 2). Micro-sample weights were 119 mg on average (min 70mg - max 288mg) for Van_PS 166
and 129 mg on average (from 120 to 150 mg) for Van_PC.
167
In LD, the micro-samples were collected 1cm lags along a 20m transect, from an area 168
at the edge of a maize field that had not been ploughed for at least 10 years. Micro-sample 169
weights were 100mg on average (77 to 144 mg).
170
For the GMV soil, the sampling procedure 1m lags along to a 20m transect, needed to 171
be adapted as it is a sandy soil and we could not proceed with cores. As the soil was very sandy, 172
10g of soil were sampled every meter at 20 spots in the field and then subsampled in the 173
laboratory (250 to 500 mg), lyophilized and stored at -80°C for DNA extraction.
174
Soil physicochemical characteristics (texture, pH, organic carbon, organic matter, C/N, 175
assimilable P, K, Ca, Mg) were measured by CESAR (CEntre Scientifique Agricole Regional, 176
Ceyzeriat, France, http://www.labo-cesar.com) on pooled micro-samples from Van_PC, 177
Van_PS, GMV and LD to get average characteristics of the site (Supplementary Table 1) using 178
a standard methods that followed the AFNOR French standard (AFNOR, 2004).
179 180
2.2. DNA extraction 181
DNA extraction from micro-samples was performed following Michelland et al. (2016).
182
Briefly, soil micro-samples were suspended (1 g mL-1) in PBS (pH=8) and centrifuged at 5,700g 183
for 1 min. The pellet was suspended in 1 mL washing solution (50mmol L-1 Tris-HCl pH = 7.7, 184
25mmol L-1 EDTA, 0.1 % SDS, 0.1 % PVP, H2O) and centrifuged at 5700g for 1min. The pellet 185
was then suspended in 35μL lysis solution (50mmol L-1 Tris-HCl, pH = 8, 25mmol L-1 EDTA, 186
3% SDS, 1.2% PVP) and microwaved at 600-700W for 45s (Orsini and Romano-Spica, 2001).
187
Four hundred microliters of extraction solution (10mmol L-1 Tris HCl, pH =8, 1mmol L-1 188
EDTA, 0.3mol L-1 sodium acetate, 1.2% PVP), pre-heated (75°C), were then added before 189
extraction with one volume phenol-choloroform-isoamylic alcohol (25:24:1) and centrifugated 190
for 10min at 13,000rpm. One volume of chloroform was then added, the solution centrifuged 191
for 5min at 13,000rpm, and the aqueous phase was retained. Ten percent of the total volume of 192
sodium acetate 3M (pH 5) and 2 volumes of cold absolute ethanol were added and, after cooling 193
on ice for 20min, the micro-samples were centrifuged for 30min at 13,000rpm and 4°C. The 194
supernatant was then discarded and the DNA pellets were washed with EtOH 70% and 195
resuspended in 20μL water.
196 197
2.3. Illumina sequencing and sequence processing 198
Amplification of the bacterial V4 region of the 16S rRNA genes was performed using the 199
universal primers 515F/806R . High-throughput sequencing was carried out after a multiplexing 200
step using a MiSeq 2x300bp PE technology (MR DNA lab, www.mrdnalab.com, Shallowater, 201
TX, USA).
202
According to the MR DNA analysis pipeline (MR DNA, Shallowater, TX, USA), paired- 203
end sequences were merged and denoising procedures, that consisted in discarding reads 204
containing ambiguous bases (N) or reads that were outside the range of expected length (i.e.
205
<150bp), were carried out using Uchime [37]. The remaining sequences were clustered at a 206
97% similarity threshold [38] with Usearch [39]. Chimeras were detected using Uchime [37]
207
and removed from the dataset. Final OTUs were taxonomically affiliated using BLASTn 208
against a cured database derived from Greengenes, RDPII and NCBI (www.ncbi.nlm.nih.gov, 209
http://rdp.cme.msu.edu) [40].
210
Singletons were removed from the dataset. In order to compare micro-samples and to 211
retain the largest possible number of micro-samples, a random resampling normalization step 212
at a depth of 5474 sequences per sample was carried out. It should be noted that this procedure 213
resulted in the exclusion of 1 sample from the Van_PC and 3 samples from the GMV soils as 214
they did not contain 5474 sequences (Table 1).
215
The sequencing data of the LD soil have been published in Michelland et al. (2016) and 216
the dataset is available under the accession number PRJEB14534 in the SRA database. Data for 217
other soils have been deposited under the following accession number ERP016178 at EBI.
218 219
2.4. Statistical analyses 220
The soil parameters (Supplementary Table 1, parameters in column) were analyzed by a 221
standardized PCA. Standardization was needed because of the differences in parameter units.
222
The data table was transformed to row percentages (row sums = 1) before doing the PCA, to 223
reduce the impact of the high sequence count variability among OTUs in the soil micro- 224
samples.
225
The table of diversity data (OTUs in columns) was analyzed by a PCoA on the Jaccard 226
distance matrix.
227
Computations were performed with the ade4 package [41] for the R Statistical Software 228
(R Core Team, 2019).
229 230
3. Results 231
3.1. Physico-chemical characteristics and bacterial diversity 232
Rarefaction curves indicated that the sequencing effort was sufficient to describe bacterial 233
diversity in Van_PC and GMV soils while in Van_PS and LD, some samples do not appear to 234
have reached an asymptote (Supplementary Figure 1). Moreover, it showed that bacterial 235
richness in the Van_PC micro-samples was more variable than in the other soils 236
(Supplementary Figure 1). There were 418 OTUs common to the four soils, equivalent to 237
16.54% of the total richness (Figure 1A).
238
The PCA ordination, based on the physical and chemical properties of the soils (texture, 239
pH, organic carbon, organic matter, C/N, assimilable P, K, Ca, Mg, Supplementary Table 1), is 240
shown in Figure 2A. The percentage variability explained by axes 1 and 2 was 59% and 27%, 241
respectively. There was a clear separation among the soils, the LD soil being related to coarse 242
silt (Csi), K and assimilable P, the GMV soil related to coarse sand, the Van_PC soil to high 243
C/N ratios, and the Van_PS soil to clay and total nitrogen. Both La Vanoise soils (i.e. Van_PC 244
and Van_PS soils) were associated with high fine silt, OM, Mg, Ca contents. The pH of the La 245
Vanoise soils was close to or above 8 (Supplementary Table 1).
246
The PCoA ordination graph of bacterial diversity data is shown in Figure 2B. The first 247
two axes account for 11% and 9% of the total variability. This analysis highlighted that GMV 248
first axis, GMV and LD soils were distinguished from the Van_PC and Van_PS soils while on 250
the second axis, GMV and LD soils were distinguished.
251 252
3.2. Abundance-occupancy and occupancy-frequency relationships 253
The abundance-occupancy diagrams for each soil showed a positive relationship between the 254
OTU average relative abundances in micro-samples and the proportion of micro-samples in 255
which they were found (Figure 3). The patterns in the occupancy-frequency diagrams were 256
different however (Figure 4). The distribution in Van_PC was unimodal with a satellite mode 257
(a large proportion of taxa occupied only one micro-sample), as highlighted by the ratio of 258
OTUs numbercore over OTUs numbersatellite equal to 0.027. Van_PS, GMV and LD presented 259
bimodal distributions with numbercoreOTUs: numbersatellite OTUs ratios of 0.301, 0.323 and 0.571, 260
respectively. Interestingly, compared to Van_PC, Van_PS and GMV, a smaller proportion of 261
the taxa that were limited to a small number of micro-samples associated with a larger 262
proportion of taxa were present in all micro-samples is characteristic of the LD soil (i.e. ratio 263
of OTUs numbercore over OTUs numbersatellite of 0.571). Van_PS, GMV and LD core taxa 264
number are always important, 105, 86 and 155 respectively but they were less numerous than 265
the satellite taxa. The average abundances (i.e. % of sequences) of satellite OTUs in micro- 266
samples were higher in Van_PC soil than in Van_PS, GMV and LD soils (Table 1), whereas 267
the average abundances of core OTUs in Van_PC soil (i.e. 21.72%) were much lower compared 268
to other soils (80.96%, 78.80% and 82.78% for Van_PS, GMV and LD soils, respectively, 269
Table 1). Core OTUs in Van_PC soil exhibited the lowest frequency compared to the other soils 270
(7.65, 9.11 and 12.86% for Van_PS, GMV and LD soils, respectively, Table 1), while satellite 271
OTUs frequency in Van_PC soil was the highest (i.e. 36.80%) compared to the other soils 272
(25.34, 28.18 and 22.49% for Van_PS, GMV and LD soils, respectively, Table 1).
273
The PCoA ordination based on OTU patterns, which explained 11 and 9% of total variability 274
on the first and second axes, respectively, separated soils with positive coordinates for LD and 275
Van_PS soils and negative coordinates for GMV and Van_PC.Moreover,on the second axis 276
(Figure 2B) it separatedLD and GMV.Moreover, the PCoA ordination based on OTU patterns 277
separated the four soils as the PCA ordination of the physico-chemical properties did (Figure 278
2). Thus, the spatial distribution type, which separates Van_PC (satellite mode) from Van_PS, 279
GMV and LD (bimodal), presents a typology that is different from the typology of soil physico- 280
chemical characteristics and to the typology based on diversity. The first axis of soil PCA 281
(which explains 59% total variability, Figure 2A) separates GMV and LD soils from Van_PS 282
and Van_PC. This corresponds to the opposition between semi-arid sandy soil of GMV and 283
organic matter rich soils from La Vanoise while LD is “positioned” by coarse silt. The second 284
axis (27% explained variability) separates LD and Van_PS from Van_PC and GMV. The 285
community spatial distribution type may thus be driven by a combination of factors different 286
from the ones driving diversity, thus bringing more information about the soil function.
287
288
3.3. Composition of core and satellite bacterial communities 289
Taxonomic investigations, at the phylum_class level (representing > 2% of sequence 290
abundance) on satellite and core OTUs, revealed that some classes, as Actinobacteria, 291
Alphaproteobacteria and Betaproteobacteria, were ubiquitous (Figure 5). Actinobacteria were 292
more abundant among GMV and LD core OTUs than among satellite OTUs, and 293
Deltaproteobacteria were less abundant among core taxa in GMV and LD than among satellite 294
taxa. Globally, Bacilli and Clostridia represented, on average, 10.2 and 0.7% of all sequences, 295
respectively, in the satellite taxa of the four soils, but only 4.1 and 2.8 %, on average, in the 296
core taxa (to be noted that Bacilli were enriched in GMV core OTUs representing 13.9% of 297
sequences).
298
Core and satellite taxa in bacterial families that accounted for > 2% of the sequences 299
were also analyzed (Table 2). Among those 52 families, 28 were specific to core taxa while 14 300
families were specific of satellite taxa (Table 2). The core specific families represented 65.9, 301
43.3, 44.3 and 33.4% of core taxa sequences in Van_PC, Van_PS, GMV and LD, respectively, 302
while the satellite-specific families represented 40.2, 14.2, 19.3 and 7.5% of satellite taxa 303
sequences in Van_PC, Van_PS, GMV and LD, respectively.
304
Among the core taxa of the four soils (357 OTUs in total), only 2.2% (i.e. 8 OTUs) were 305
common to all of them (Figure 1B): Acidimicrobiales spp. (affiliated with Actinobacteria), 306
Azospira spp. (affiliated with Betaproteobacteria, Rhodocyclales order), Flexibacter spp.
307
(affiliated with Bacteroidetes, Cytophagales order), Gemmatimonas spp. (affiliated with 308
Gemmatimonadetes), Nitrosococcus spp. (affiliated with Gammaproteobacteria, Chromatiales 309
order), Rubrobacter spp (affiliated with Actinobacteria, Rubrobacterales order), Sphaerobacter 310
thermophilus (affiliated with Chloroflexi, Sphaerobacterales order), Sphingomonas spp.
311
(affiliated with Alphaproteobacteria, Sphingomonadales order). Moreover, Van_PS, GMV and 312
LD soils had 9.2%, 9.5% and 23.2% specific core taxa, respectively, whereas Van_PC 313
presented no specific core taxa (Table 1, Figure 1B). Relatively to common taxa retrieved in 314
the four soils (i.e. 418 OTUs, Figure 1A), core OTUs in Van_PC which is satellite mode 315
represented 2.4% of OTUs and in bimodal soils, Van_PS , GMV and LD soil it represented 316
24.1, 15.6% and 28.7% of OTUs respectively (Table 1).
317
In the same way, among the 1288 OTUs constituting the satellite taxa retrieved in the 318
four soils (Figure 1C), only 2 OTUs were common to the four soils (i.e. 0.16% of OTUs):
319
Ancalomicrobium adetum (affiliated with Alphaproteobacteria, Rhizobiales order) and 320
Nonomuraea terrinata (affiliated with Actinobacteria, Actinomycetales order). Moreover, 321
Van_PC, Van_PS, GMV and LD soils had 20.3%, 17.6, 13.9 and 14.4% specific satellite taxa, 322
respectively (Table 1, Figure 1C). Relatively to common taxa retrieved in the four soils (i.e.
323
418 OTUs, Figure 1A), satellite OTUs in Van_PC, Van_PS, GMV and LD represented 11.2, 324
3.8, 18.9 and 8.4% of OTUs, respectively (Table 1).
325 326
3.4. Bacterial ecological strategy at the small scale: habitat generalists and specialists 327
Concerning generalists, among core taxa retrieved in the four studied soils (i.e. 229 OTUs), 328
34.7% were common to at least two soils, 20.2% common to at least 3 soils and only 3.5%
329
common to the four soils (Figure 1B).
330
Fifty-three specialist OTUs were found in Van_PC, representing 4.8% of the total richness in 331
Van_PC (Table 1). To be noted that in Van_PC, 45.3% of specialist taxa belonged to 332
Proteobacteria and 15.1% belonged to Actinobacteria. In GMV, 9 OTUs were considered 333
specialist, whilst there were 2 in Van_PS and only 1 in LD (Table 1). Interestingly, specialists 334
OTUs in LD and GMV are specific to these soils while one specialist OTU was common to 335
Van-PC and Van_PS soils.
336 337
4. Discussion 338
We use a community ecology approach, drawn from the study of macroorganisms, was 339
used to investigate bacterial community spatial distributions at the micro-scale across four 340
different soils. The micro-sampling (few hundred milligrams: from 100 to 500mg) was carried 341
out on undisturbed soil so as to take the spatial distribution of taxa in the micro-samples into 342
account (within and between micro-samples). The analysis of bacterial diversity data combined 343
with soil micro sampling suggested that bacteria can be classified in each soil as core or satellite 344
taxa, depending on their distribution at the small scale.
345 346
4.1. Spatial distribution patterns 347
The separation of the soils on the two PCA and PCoA ordinations, suggests that satellite mode 348
distribution, retrieved in Van_PC, is related to coarse sand, high C/N and pH. Unfortunately, 349
we could not directly correlate bacterial taxa with environmental variables as micro-samples 350
were too small to perform physico-chemical analyses. To date, quantifying environmental 351
variables in micro-samples remains a big technical issue which needed to be resolved in further 352
decades to gain insight into relationships between microbial communities and fine soil physico- 353
chemical properties. Moreover, other complementary variables could be drivers of community 354
spatial structure such as bulk density, soil structural heterogeneity, pore size distributions and 355
connectivity and organic features (i.e. root architecture or soil invertebrate activity).
356
While the existence of a rare microbial biosphere, across different ecosystems, is now 357
accepted [43, 44], there is no widely accepted definition of rare taxa in the literature. Indeed, 358
arbitrary relative abundance cut-offs, depending on the study, range from 0.1% to 0.01% [45].
359
In the present work, conducted at the small scale, we used a spatial definition of rare taxa based 360
on micro-sample occupancy, where taxa found in only one micro-sample were considered 361
spatially rare, or satellite taxa [32]). This corresponds to an average relative abundance of 362
0.027% of the total sequences, when including all micro-samples in this study. Compared to 363
the definition of Lynch and Neufeld (2015), our percentage of rare taxa identified is quite 364
conservative. Because rare taxa are substantial contributors to bacterial community ecology 365
[43, 45], their spatial distribution may be an important factor impacting ecological processes 366
and thus soil functioning. Studies focusing on spatial ecology of soil bacteria [46] concluded 367
that the relative importance of the underlying processes contributing to the establishment of 368
bacterial distributions can change with soil characteristics. More generally, Lindh et al. (2017) 369
suggested that analyses of occupancy-frequency patterns can be a highly valuable approach 370
allowing the definition of microbial biomes across environmental gradients [7].
371
Although a positive small scale abundance-occupancy relationship has already been 372
identified in the LD soil [8], this study confirmed this relationship and extended it to three other 373
soils, suggesting a certain prevalence of the relationship in soil microbial communities. This is 374
hardly surprising as it is one of the most fundamental patterns in ecology [2]. Moreover, the 375
bacterial community spatial distributions also conformed to Hanski’s core and satellite 376
hypothesis [29]. However, the present work was conducted in four soils for which samples 377
differed a bit in the size and/or mass collected as there was a loamy, a silty loam and a sandy 378
soil that do not allow the strictly same sampling procedure. Soil heterogeneity and thus, the 379
requirement for different experimental procedures may have an impact on the results.
380
Gleason (1929) showed that quadrats of the most serviceable size should be chosen to 381
measure frequency distributions of plants, and that the information is obscured or lost if the 382
quadrats are either too large or too small [47]. The fact that different ecological strategies were 383
identifiable here suggests that the sample size used was appropriate for the study of bacterial 384
spatial distributions in soils [16]. Compared to other studies, the present work showed the 385
relevance of the small scale approach in spite of the very low organism’s size to sample size 386
ratio [48]. Previous studies investigating the type of occupancy-frequency patterns have 387
suggested that unimodal distributions, that are characterized by an excess of rare, endemic 388
species, occur more frequently than bimodal distributions [49, 50]. Even when bimodal 389
distributions are detected, the magnitude of the core mode, representing cosmopolitan species, 390
is generally smaller than that of the rare species [50]. The three bimodal distributions that we 391
observed, indicate that it may be a common community spatial distribution of soil bacteria at 392
the small scale. This suggested that OTUs are either adapted to the majority of micro-habitats 393
in this soil, or that these OTUs have a high dispersal capabilities favouring the colonization of 394
empty ecological niches. Indeed, core species are believed to owe their regional commonness 395
to the rescue effect (i.e. reduction in local extinction probability [51]) while satellite species are 396
maintained by the habitat heterogeneity [52]. Cadotte and Lovett-Douste (2007) suggested that, 397
via the rescue effect, widespread and abundant species should have reduced risk of local 398
extinction. In their work conducted on trees, different community spatial structures were 399
observed [53]. Indeed, trees in degraded environments showed a satellite mode distribution, 400
contrary to the other studied environments. In the Van_PC soil, the satellite mode distribution 401
might represent a functional mode (i.e. corresponding to a stressful state) as in Cadotte and 402
Lovett-Douste (2007).
403 404
4.2. Bacterial ecological strategies 405
For some authors, identifying core taxa is the first step in defining a “healthy” community and 406
predicting community responses to perturbation [54]. Interestingly, it has been suggested that 407
due to the great microbial diversity among ecosystems, the possibility of any species being 408
present at high abundance in a large range of samples is low and it is possible that the focus 409
should instead be on higher taxonomic levels or on functional genes [55]. The results presented 410
here are in accordance with this theory as only a few core OTUs are common to the four soils.
411
However, when considering the commonness in only two or three soils, their richness increases, 412
suggesting a patchy distribution rather than a ubiquitous one.
413 414 415 416
5. Conclusions 417
There is an increasing recognition that interactions within bacterial communities can have an 418
impact on community functioning [56, 57]. In this contribution we have shown that the types 419
of small-scale distributions of bacterial communities vary across soils with different physico- 420
chemical features. This suggests that the type and range of interactions that might be expected 421
context. We have shown that one soil presented a core mode, another presented a satellite mode 423
and the three others presented bimodal distributions. We also identify among bacterial richness 424
retrieved in those four soils micro-samples that up to 13% can be classified as core taxa, that 425
could be responsible for the stability of soil bacterial communities. Thus the considering of 426
microbial spatial distribution, that could play a major ecological role, should further improve 427
predictions and modelling on diversity modification risks, on soil bio-conservation and 428
epidemiology.
429
Conflicts of interest 430
The authors declare that they have no known competing financial interests or personal 431
relationships that could have appeared to influence the work reported in this paper.
432 433
Funding 434
This work was cofunded by the EC2CO MicrobiEn AO2012- 779949 ‘Les communautés 435
bactériennes dans les sols extrêmes: les paramètres de leur structure et leur composition’ and 436
the Labex DRIIHM, French program "Investissements d'Avenir" (ANR-11-LABX-0010) 437
which is managed by the ANR. We thank La Vanoise National Parc for soil sampling 438
authorization.
439 440
References 441
1. Delgado-Baquerizo M, Maestre FT, Reich PB, et al (2016) Microbial diversity drives 442
multifunctionality in terrestrial ecosystems. Nat Commun 7:10541.
443
https://doi.org/10.1038/ncomms10541 444
2. Gaston KJ, Blackburn TM, Greenwood JJD, et al (2000) Abundance–occupancy 445
relationships. J Appl Ecol 37:39–59. https://doi.org/10.1046/j.1365-2664.2000.00485.x 446
3. Graham EB, Knelman JE, Schindlbacher A, et al (2016) Microbes as Engines of 447
Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem 448
Processes? Front Microbiol 7:. https://doi.org/10.3389/fmicb.2016.00214 449
4. Hubbell SP (2001) The Unified Neutral Theory of Biodiversity and Biogeography 450
(MPB-32). Princet Univ Press 451
5. Cariveau DP, Elijah Powell J, Koch H, et al (2014) Variation in gut microbial 452
communities and its association with pathogen infection in wild bumble bees (Bombus). ISME 453
J 8:2369–2379. https://doi.org/10.1038/ismej.2014.68 454
6. Hugoni M, Escalas A, Bernard C, et al (2018) Spatiotemporal variations in microbial 455
diversity across the three domains of life in a tropical thalassohaline lake (Dziani Dzaha, 456
Mayotte Island). Mol Ecol 27:4775–4786 457
7. Lindh MV, Sjöstedt J, Ekstam B, et al (2017) Metapopulation theory identifies 458
biogeographical patterns among core and satellite marine bacteria scaling from tens to 459
thousands of kilometers. Environ Microbiol 19:1222–1236. https://doi.org/10.1111/1462- 460
2920.13650 461
8. Michelland R, Thioulouse J, Kyselková M, Grundmann GL (2016) Bacterial 462
Community Structure at the Microscale in Two Different Soils. Microb Ecol 72:717–724.
463
https://doi.org/10.1007/s00248-016-0810-0 464
9. Cutler NA, Chaput DL, Oliver AE, Viles HA (2015) The spatial organization and 465
microbial community structure of an epilithic biofilm. FEMS Microbiol Ecol 91:.
466
https://doi.org/10.1093/femsec/fiu027 467
10. Jones SE, Lennon JT (2010) Dormancy contributes to the maintenance of microbial 468
diversity. Proc Natl Acad Sci U S A 107:5881–5886. https://doi.org/10.1073/pnas.0912765107 469
11. Dohrmann AB, Küting M, Jünemann S, et al (2013) Importance of rare taxa for bacterial 470
diversity in the rhizosphere of Bt- and conventional maize varieties. ISME J 7:37–49.
471
https://doi.org/10.1038/ismej.2012.77 472
12. Louca S, Polz MF, Mazel F, et al (2018) Function and functional redundancy in 473
microbial systems. Nat Ecol Evol 2:936–943. https://doi.org/10.1038/s41559-018-0519-1 474
13. Galand PE, Casamayor EO, Kirchman DL, Lovejoy C (2009) Ecology of the rare 475
microbial biosphere of the Arctic Ocean. Proc Natl Acad Sci U S A 106:22427–22432.
476
https://doi.org/10.1073/pnas.0908284106 477
14. Hugoni M, Taib N, Debroas D, et al (2013) Structure of the rare archaeal biosphere and 478
seasonal dynamics of active ecotypes in surface coastal waters. Proc Natl Acad Sci U S A 479
110:6004–6009. https://doi.org/10.1073/pnas.1216863110 480
15. Franklin RB, Mills AL (2003) Multi-scale variation in spatial heterogeneity for 481
microbial community structure in an eastern Virginia agricultural field. FEMS Microbiol Ecol 482
44:335–346. https://doi.org/10.1016/S0168-6496(03)00074-6 483
16. Grundmann GL (2004) Spatial scales of soil bacterial diversity--the size of a clone.
484
FEMS Microbiol Ecol 48:119–127. https://doi.org/10.1016/j.femsec.2004.01.010 485
17. Morris SJ, Boerner REJ (1999) Spatial distribution of fungal and bacterial biomass in 486
southern Ohio hardwood forest soils: scale dependency and landscape patterns. Soil Biol 487
Biochem 31:887–902. https://doi.org/10.1016/S0038-0717(99)00002-4 488
18. Wilpiszeski RL, Aufrecht JA, Retterer ST, et al (2019) Soil Aggregate Microbial 489
Communities: Towards Understanding Microbiome Interactions at Biologically Relevant 490
Scales. Appl Environ Microbiol 85:. https://doi.org/10.1128/AEM.00324-19 491
19. Nunan N, Schmidt H, Raynaud X (2020) The ecology of heterogeneity: soil bacterial 492
communities and C dynamics. Philos Trans R Soc Lond B Biol Sci 375:20190249.
493
https://doi.org/10.1098/rstb.2019.0249 494
20. Bailey VL, McCue LA, Fansler SJ, et al (2013) Micrometer-scale physical structure and 495
microbial composition of soil macroaggregates. Soil Biol Biochem 65:60–68.
496
https://doi.org/10.1016/j.soilbio.2013.02.005 497
21. Blaud A, Chevallier T, Virto I, et al (2014) Bacterial community structure in soil 498
microaggregates and on particulate organic matter fractions located outside or inside soil 499
macroaggregates. Pedobiologia 57:191–194. https://doi.org/10.1016/j.pedobi.2014.03.005 500
22. Davinic M, Fultz LM, Acosta-Martinez V, et al (2012) Pyrosequencing and mid- 501
infrared spectroscopy reveal distinct aggregate stratification of soil bacterial communities and 502
organic matter composition. Soil Biol Biochem 46:63–72.
503
https://doi.org/10.1016/j.soilbio.2011.11.012 504
23. Kravchenko AN, Negassa W, Guber AK, Schmidt S (2014) New Approach to Measure 505
Soil Particulate Organic Matter in Intact Samples Using X-ray Computed Microtomography.
506
Soil Sci Soc Am J 78:1177–1185. https://doi.org/10.2136/sssaj2014.01.0039 507
24. Mummey D, Holben W, Six J, Stahl P (2006) Spatial Stratification of Soil Bacterial 508
Populations in Aggregates of Diverse Soils. Microb Ecol 51:404–411.
509
https://doi.org/10.1007/s00248-006-9020-5 510
25. Negassa WC, Guber AK, Kravchenko AN, et al (2015) Properties of Soil Pore Space 511
Regulate Pathways of Plant Residue Decomposition and Community Structure of Associated 512
Bacteria. PLOS ONE 10:e0123999. https://doi.org/10.1371/journal.pone.0123999 513
26. Rillig MC, Muller LA, Lehmann A (2017) Soil aggregates as massively concurrent 514
evolutionary incubators. ISME J 11:1943–1948. https://doi.org/10.1038/ismej.2017.56 515
27. Barberán A, Bates ST, Casamayor EO, Fierer N (2012) Using network analysis to 516
explore co-occurrence patterns in soil microbial communities. ISME J 6:343–351.
517
https://doi.org/10.1038/ismej.2011.119 518
28. Pandit SN, Kolasa J, Cottenie K (2009) Contrasts between habitat generalists and 519
specialists: an empirical extension to the basic metacommunity framework. Ecology 90:2253–
520
2262. https://doi.org/10.1890/08-0851.1 521
29. Hanski I (1982) Dynamics of Regional Distribution: The Core and Satellite Species 522
Hypothesis. Oikos 38:210–221. https://doi.org/10.2307/3544021 523
30. Ulrich W, Zalewski M (2006) Abundance and co-occurrence patterns of core and 524
satellite species of ground beetles on small lake islands. Oikos 114:338–348.
525
https://doi.org/10.1111/j.2006.0030-1299.14773.x 526
31. Chazdon RL, Chao A, Colwell RK, et al (2011) A novel statistical method for 527
classifying habitat generalists and specialists. Ecology 92:1332–1343.
528
https://doi.org/10.1890/10-1345.1 529
32. Monard C, Gantner S, Bertilsson S, et al (2016) Habitat generalists and specialists in 530
microbial communities across a terrestrial-freshwater gradient. Sci Rep 6:37719.
531
https://doi.org/10.1038/srep37719 532
33. Bahram M, Hildebrand F, Forslund SK, et al (2018) Structure and function of the global 533
topsoil microbiome. Nature 560:233–237. https://doi.org/10.1038/s41586-018-0386-6 534
34. Wang C, Michalet R, Liu Z, et al (2020) Disentangling Large- and Small-Scale Abiotic 535
and Biotic Factors Shaping Soil Microbial Communities in an Alpine Cushion Plant System.
536
Front Microbiol 11:. https://doi.org/10.3389/fmicb.2020.00925 537
35. Martínez-Olivas MA, Jiménez-Bueno NG, Hernández-García JA, et al (2019) Bacterial 538
and archaeal spatial distribution and its environmental drivers in an extremely haloalkaline soil 539
at the landscape scale. PeerJ 7:e6127. https://doi.org/10.7717/peerj.6127 540
36. Taş N, Prestat E, Wang S, et al (2018) Landscape topography structures the soil 541
microbiome in arctic polygonal tundra. Nat Commun 9:777. https://doi.org/10.1038/s41467- 542
018-03089-z 543
37. Edgar RC, Haas BJ, Clemente JC, et al (2011) UCHIME improves sensitivity and speed 544
of chimera detection. Bioinformatics 27:2194–2200.
545
https://doi.org/10.1093/bioinformatics/btr381 546
38. Kim M, Morrison M, Yu Z (2011) Evaluation of different partial 16S rRNA gene 547
sequence regions for phylogenetic analysis of microbiomes. J Microbiol Methods 84:81–87.
548
https://doi.org/10.1016/j.mimet.2010.10.020 549
39. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST.
550
Bioinformatics btq461. https://doi.org/10.1093/bioinformatics/btq461 551
40. DeSantis TZ, Hugenholtz P, Larsen N, et al (2006) Greengenes, a Chimera-Checked 552
16S rRNA Gene Database and Workbench Compatible with ARB. Appl Environ Microbiol 553
72:5069–5072. https://doi.org/10.1128/AEM.03006-05 554
41. Thioulouse J, Dray S, Dufour A-B, et al (2018) Multivariate Analysis of Ecological 555
Data with ade4. Springer-Verlag, New York 556
42. R Core Team (2016) R: A Language and Environment for Statistical Computing. R 557
Foundation for Statistical Computing, Vienna, Austria 558
43. Pedrós-Alió C (2012) The rare bacterial biosphere. Annu Rev Mar Sci 4:449–466.
559
https://doi.org/10.1146/annurev-marine-120710-100948 560
44. Sogin ML, Morrison HG, Huber JA, et al (2006) Microbial diversity in the deep sea and 561
the underexplored “rare biosphere.” Proc Natl Acad Sci 103:12115–12120.
562
https://doi.org/10.1073/pnas.0605127103 563
45. Lynch MDJ, Neufeld JD (2015) Ecology and exploration of the rare biosphere. Nat Rev 564
Microbiol 13:217–229. https://doi.org/10.1038/nrmicro3400 565
46. Raynaud X, Nunan N (2014) Spatial Ecology of Bacteria at the Microscale in Soil.
566
PLOS ONE 9:e87217. https://doi.org/10.1371/journal.pone.0087217 567
47. Gleason HA (1929) The Significance of Raunkiaer’s Law of Frequency. Ecology 568
10:406–408. https://doi.org/10.2307/1931149 569
48. Fierer N, Lennon JT (2011) The generation and maintenance of diversity in microbial 570
communities. Am J Bot 98:439–448. https://doi.org/10.3732/ajb.1000498 571
49. Levin SA (1974) Dispersion and Population Interactions. Am Nat 108:207–228.
572
https://doi.org/10.1086/282900 573
50. Tokeshi M (1992) Dynamics of distribution in animal communities: Theory and 574
analysis. Res Popul Ecol 34:249–273. https://doi.org/10.1007/BF02514796 575
51. Eriksson A, Elías-Wolff F, Mehlig B, Manica A (2014) The emergence of the rescue 576
effect from explicit within- and between-patch dynamics in a metapopulation. Proc R Soc B 577
Biol Sci 281:20133127. https://doi.org/10.1098/rspb.2013.3127 578
52. Czaran T (1998) Spatiotemporal Models of Population and Community Dynamics.
579
Springer US 580
53. Cadotte MW, Lovett-Doust J (2007) Core and Satellite Species in Degraded Habitats:
581
an Analysis Using Malagasy Tree Communities. Biodivers Conserv 16:2515–2529.
582
https://doi.org/10.1007/s10531-006-9027-8 583
54. Shade A, Handelsman J (2012) Beyond the Venn diagram: the hunt for a core 584
microbiome. Environ Microbiol 14:4–12. https://doi.org/10.1111/j.1462-2920.2011.02585.x 585
55. Hamady M, Knight R (2009) Microbial community profiling for human microbiome 586
projects: Tools, techniques, and challenges. Genome Res 19:1141–1152.
587
https://doi.org/10.1101/gr.085464.108 588
56. Pande S, Kost C (2017) Bacterial Unculturability and the Formation of Intercellular 589
Metabolic Networks. Trends Microbiol 25:349–361. https://doi.org/10.1016/j.tim.2017.02.015 590
57. Pascual-García A, Bonhoeffer S, Bell T (2020) Metabolically cohesive microbial 591
consortia and ecosystem functioning. Philos Trans R Soc B Biol Sci 375:20190245.
592
https://doi.org/10.1098/rstb.2019.0245 593
594 595 596 597
Figures and Tables Legends 598
Figure 1.Venn Diagrams presenting the shared and specific OTUs (A) detected in the four 599
soils, (B) among core OTUs and (C) among satellite OTUs.
600
Figure 2. PCA map of soils characteristics (A) and PCoA map of bacterial diversity (B) for 601
LD, GMV, Van_PC, Van_PS soils. On Figure 2A, arrows correspond to the four soil samples 602
and labels to the soil physico-chemical parameters. On Figure 2B, the position of ellipse centers 603
are given by the means of the coordinates of micro-samples on the two axes of the factor map 604
for each soil sample origins (LD, GMV, Van_PC and Van_PS). The width and height of ellipses 605
are given by the variance of the coordinates of the micro-samples on the two axes and the ellipse 606
slope is equal to their covariance.
607
Figure 3. Relationship between relative abundance (average percentage taxa presence in micro- 608
samples) and occupancy (percentage of micro-samples in which each taxa is present) in 609
Van_PC, Van_PS, LD and GMV soils.
610
Figure 4. Occupancy-frequency relationship for Van_PC, Van_PS, LD and GMV soils.
611
Frequency is expressed as the percent taxa found for each possible occupancy.
612
Figure 5. Distribution of bacterial classes representing more than 2% of total taxa in core and 613
satellite taxa, in Van_PC, Van_PS, LD and GMV soils.
614 615
Table 1. Sequence analyses for Van_PC, Van_PS, LD and GMV soils: sample number, raw 616
sequence number and normalization threshold. Detailed analyses of core and satellite taxa:
617
OTUs number, sequences percentage, frequency, average abundance. Detailed analyses of 618
ecological strategies: specific core OTUs percentage, percentage of core OTUs found in 619
common taxa and number of specialist OTUs.
620
Table 2.Distribution of bacterial families representing more than 2% of sequences in at least 621
one of the condition (core or satellite of each site). The ten most abundant families per 622
conditions were highlighted in grey.
623
Supplementary Figure 1. Rarefaction curves for Van_PC, Van_PS, LD and GMV soils, using 624
the normalized datasets (i.e. 5474 sequences).
625
Supplementary Table 1. Physico-chemical characteristics of Van_PC, Van_PS, LD and GMV 626
soils. The unit is g.kg-1 except for pH and C/N ratio.
627
Supplementary Table 2. La Vanoise soil sampling: Distances between micro-samples (cores) 628
in Van_PC and in Van _PS starting from an initial point, named 0 m. Micro-samples were taken 629
along two transects 10 cm apart, in September 2013 and September 2014. Results obtained on 630
all micro-samples were gathered as no difference could be shown between the two sampling 631
dates. Micro-samples named “a” corresponded to the top 1 mm of the cores and sample “b”
632
corresponded to the 1 to 2 mm below sample “a”. “b*”: in two cases, a second core was taken 633
beside core “b”, at the same distance from the origin.
634 635 636 637 638 639 640 641 642 643 644 645 646
648
649 650 651
Figure 1.Venn Diagrams presenting the shared and specific OTUs (A) detected in the four 652
soils, (B) among core OTUs and (C) among satellite OTUs.
653 654 655 656
657 658
Figure 2. PCA map of soils characteristics (A) and PCoA map of bacterial diversity (B) for 659
LD, GMV, Van_PC, Van_PS soils. On Figure 2A, arrows correspond to the four soil samples 660
and labels to the soil physico-chemical parameters. On Figure 2B, the position of ellipse centers 661
are given by the means of the coordinates of micro-samples on the two axes of the factor map 662
for each soil sample origins (LD, GMV, Van_PC and Van_PS). The width and height of ellipses 663
are given by the variance of the coordinates of the micro-samples on the two axes and the ellipse 664
slope is equal to their covariance.
665 666 667 668 669
670
Figure 3. Relationship between relative abundance (average percentage taxa presence in micro- 671
samples) and occupancy (percentage of micro-samples in which each taxa is present) in 672
Van_PC, Van_PS, LD and GMV soils.
673 674
675
Figure 4. Occupancy-frequency relationship for Van_PC, Van_PS, LD and GMV soils.
676
Frequency is expressed as the percent taxa found for each possible occupancy.
677
678
Figure 5. Distribution of bacterial classes representing more than 2% of total taxa in core and 679
satellite taxa, in Van_PC, Van_PS, LD and GMV soils.
680
682 683 684
685
Supplementary Figure 1. Rarefaction curves for Van_PC, Van_PS, LD and GMV soils, using 686
the normalized datasets (i.e. 5474 sequences).
687 688 689 690 691 692 693 694
Supplementary Table 1. Physico-chemical characteristics of Van_PC, Van_PS, LD and GMV 695
soils. The unit is g.kg-1 except for pH and C/N ratio.
696
697
Supplementary Table 2. La Vanoise soil sampling: Distances between micro-samples (cores) 698
in Van_PC and in Van _PS starting from an initial point, named 0 m. Micro-samples were taken 699
along two transects 10 cm apart, in September 2013 and September 2014. Results obtained on 700
all micro-samples were gathered as no difference could be shown between the two sampling 701
dates. Micro-samples named “a” corresponded to the top 1 mm of the cores and sample “b”
702
corresponded to the 1 to 2 mm below sample “a”. “b*”: in two cases, a second core was taken 703
beside core “b”, at the same distance from the origin.
704
705
LD Van_PS Van_PC GMV
Coarse sand 40 127 179 437
Fine sand 392 115 140 476
Coarse silt 337 105 149 38
Fine silt 124 483 458 13
Clay 107 170 74 35
pH 6.82 7.77 8.41 6.61
Organic C 9.1 40.6 30.3 1.9
Organic matter 15.6 69.9 52.2 3.4
Total N 0.9 2.9 0.7 0.2
C/N 10 13 45 9
Assimilable P 0.261 0.032 0.013 0
K 0.138 0.056 0.015 0.044
Ca 1.94 9.54 9.1 0.33
Mg 0.055 0.62 0.174 0.084
Van_PC (17 samples) 0 m 0.3 m 0.6 m 0.9 m 1.2 m 1.5 m 1.8 m 2.1 m 2.4 m 2.7 m 3 m 3.3 m
September 2013 a, b b, b* b b b b
September 2014 a, b, b* b b b b b b
Van_PS (22 samples) 0 m 0.32 m 1 m 2 m 2.5 m 3 m 3.5 m 4 m 4.5 m 5 m 7 m 9 m 10 m
September 2013 b b b b b b b b
September 2014 a, b a, b b b b b b b b b b b
Distance, meter
